Sales teams use many systems and applications to run their operations. There are CRM, SFA, order management and billing applications to help capture customer and account information and manage various customer processes. These applications help sales to manage their day-to-day tasks, but are these tools helping them sell more?
Why is it that even today, sales teams feel they don’t have enough actionable, timely and contextual information to offer the right solution to a prospect and close the deal? Why is it that selling remains more of an art form than a repeatable scientific method?
Of course, people play a large role there. They’re unpredictable, and everyone is different. And whether you’re in B2B or B2C, selling is always P2P – people to people. Even though sales, in part, remains an art form, there are possibilities where technology can assist teams to be more effective, efficient and consistent.
Most sales people are hungry for reliable information – information that gives them an edge over their competition. Information that helps them understand the customer better and propose the right solution faster. Sales wants to acquire reliable information quickly and make their formula of success repeatable. Technology can help.
Today, modern data management platforms are not only providing clean and current customer data, but also incorporating technologies like a graph, predictive analytics and machine learning to provide relevant insights and intelligent recommendations to sales users.
Graph technology, in essence, helps sales uncover the many-to-many relationships between contacts, accounts, products, locations and contracts. And understanding relationships is critical to sales – connecting all business entities in many-to-many relationships reveals deeper insights into the client’s needs and preferences. You can quickly identify a customer’s channel and content preferences, the products they currently use, their location and influence in a department and involvement in other teams and committees.
When you combine customer profile data with relationship information and interaction and transactional data, new possibilities emerge. With various predictive and machine learning techniques, you can start uncovering new approaches to sales. Graphs in conjunction with analytics platforms like Apache Spark can make selling much more efficient. Big data analytics environments can handle complex queries and manage large datasets. Sales can determine the top-selling products in each of the target segments. Graph libraries can be used to group customer contacts based on their departments, locations and product usage, and predictive analytics can be used to determine the best day, time and channel for next engagement with the customer.
Sales needs access to clean and accurate customer data. If there are duplicates and data with vague information, they start compiling information in their own shadow applications, like Excel. Graph analytics can help you identify if the customer engaging through multiple channels or working at various locations is the same person or not. Additional evidence via relations helps decide if entities in different data sources like CRM, billing and shipping are the same. This helps keep the data complete, current and clean.
Analytics can also be used to determine who are the influencers and key interested parties in the organization. Advanced algorithms such as page ranks, triangles, node connectivity and node degree can help sales learn more about the relationships between data entities. You can determine product success, stakeholder relationship strengths and the influence of a contact.
Once complete influencer information is compiled into a graph, with all their connections and relationships with other entities like job titles, committee memberships and purchase records, PageRank algorithms can determine a stakeholder’s influence in a deal or recommend where to focus when navigating the complex organizational structures.
Graph analytics can help you identify new relationships in the account that can assist in determining new selling opportunities and closing deals faster. If there’s a new decision-maker joining an organization, intelligent recommendations can alert sales, suggesting a visit and the ways to best engage with them.
Intelligent recommendations, based on a customer’s data and preferences, can guide sales with the best time and channel to call or visit a client. It can also suggest the most relevant content or offer to send to the customer. This not only enables sales to provide the most relevant information to the customer, but also brings consistency of experience across the sales team.
Additional use cases include recommending sales team alignments (zip-terr-product,) analyzing account hierarchies to recommend how to expand in an account and which products to offer, and identifying the business units that are using competitor products and identify up-sell and cross-sell opportunities.
Incorporating machine learning technology into your data management platform also helps you find new ways to match and merge your data better. The system can identify new match rules and inform to sales operations or data stewards which new rules can be used for better data matching. It also monitors the manual merges and unmerges of customer profiles and learns from it to continuously improve matching.
Businesses are still quite skeptical of the black box machine learning that takes actions without telling users the reason why. It’s important, as a first step, for machine learning systems to offer suggestions to users so they can understand and then adopt the newly discovered rules and enable automated actions. Modern data management with graph technology, predictive analytics and machine learning can take your sales effectiveness to a new level. Sales teams can connect to their customers with confidence and ensure they’re taking the right sales approach for the right customer and are assembling the right offers to meet customer needs.